Dissociating respiratory depression and analgesia via a data-driven model of interacting respiratory and pain networks

项目总结

PROJECT SUMMARY This mentored research scientist development award will allow the candidate, Dr. Ryan Phillips, to establish an independent research career focused on understanding the complex circuit interactions that link pain and respiration. The training plan outlined in this award, combined with the candidate's background in brainstem pain circuits and computational modelling, make him ideally suited to successfully follow this career path. Opioid receptors are expressed in multiple brainstem regions that regulate pain and respiration, and the link between opioid-induced analgesia and respiratory depression poses a major clinical challenge. Indeed, the often difficult to predict side-effect of opioid-induced respiratory depression (OIRD) is the leading cause of opioid-related deaths driving the current opioid epidemic. Despite this public health significance, the mechanisms underlying OIRD are not fully understood and therapeutic strategies to prevent or reverse OIRD while maintaining analgesia are limited. A natural idea is that OIRD results from direct effects of opioids on respiratory rhythm-generating neurons of the pre-Bötzinger Complex (preBötC) in the ventral medulla. Although some preBötC neurons do express µ-opioid receptors (µORs), encoded by the Oprm1 gene, systemic opioids continue to produce respiratory depression and apneas even after genetic deletion of µORs in this population. Importantly, the contributions other µORs/Oprm1 expressing respiratory- and pain-related neurocircuitries to OIRD have been described. These brainstem regions, including the parabrachial (PB), Kolliker Fuse (KF), and rostral ventromedial medulla (RVM), all have important roles in regulating the respiratory rhythm generated by the preBötC. However, how opioids alter the network dynamics of these interacting regions remains unknown. We hypothesize that interruption of opioid sensitive network interactions is a major mechanism underlying OIRD caused by systemic opioids. This hypothesis will be tested using computational modeling and experimental approaches performed in tandem. Computational modeling has played a significant role in advancing the field of respiratory neurobiology and is ideally suited for examining the implications of disparate datasets and for low-cost, high throughput testing of hypothesized network features. In addition to existing data, our model will be guided and constrained by new cell-type- and projection- specific experimental manipulations in vivo that will constitute a significant training aspect of this award. By combining these strategies, our data-driven computational model will yield novel insights into the network dynamics that underlie opioid-induced respiratory depression as well as potential therapeutic targets to generate pain relief without respiratory side effects.